Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Kevin H. Leung"'
Autor:
Kevin H. Leung, Steven P. Rowe, Jeffrey P. Leal, Saeed Ashrafinia, Mohammad S. Sadaghiani, Hyun Woo Chung, Pejman Dalaie, Rima Tulbah, Yafu Yin, Ryan VanDenBerg, Rudolf A. Werner, Kenneth J. Pienta, Michael A. Gorin, Yong Du, Martin G. Pomper
Publikováno v:
EJNMMI Research, Vol 12, Iss 1, Pp 1-15 (2022)
Abstract Background Accurate classification of sites of interest on prostate-specific membrane antigen (PSMA) positron emission tomography (PET) images is an important diagnostic requirement for the differentiation of prostate cancer (PCa) from foci
Externí odkaz:
https://doaj.org/article/b41941de8d2b40b8a18526ea80bd1b9d
Publikováno v:
EJNMMI Research, Vol 11, Iss 1, Pp 1-14 (2021)
Abstract Background Diagnosis of Parkinson’s disease (PD) is informed by the presence of progressive motor and non-motor symptoms and by imaging dopamine transporter with [123I]ioflupane (DaTscan). Deep learning and ensemble methods have recently s
Externí odkaz:
https://doaj.org/article/0114f73f3c7e48a4987dcb6ed3fe72c3
Publikováno v:
Diagnostics, Vol 12, Iss 8, p 1945 (2022)
While machine learning (ML) methods may significantly improve image quality for SPECT imaging for the diagnosis and monitoring of Parkinson’s disease (PD), they require a large amount of data for training. It is often difficult to collect a large p
Externí odkaz:
https://doaj.org/article/49c3c39d457048178eda7d7a431de772
Publikováno v:
EJNMMI Research, Vol 11, Iss 1, Pp 1-14 (2021)
EJNMMI Research
EJNMMI Research
Background Diagnosis of Parkinson’s disease (PD) is informed by the presence of progressive motor and non-motor symptoms and by imaging dopamine transporter with [123I]ioflupane (DaTscan). Deep learning and ensemble methods have recently shown prom
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2d98a0e716a15b3ed62d6131c740ffb3
https://doaj.org/article/0114f73f3c7e48a4987dcb6ed3fe72c3
https://doaj.org/article/0114f73f3c7e48a4987dcb6ed3fe72c3
Autor:
Kevin H. Leung, Parham Geramifar, Arman Rahmim, Pardis Ghafarian, Mehrdad Oveisi, Mohammad Reza Ay, Mostafa Ghelich-Oghli, Isaac Shiri
Publikováno v:
European Radiology. 29:6867-6879
To obtain attenuation-corrected PET images directly from non-attenuation-corrected images using a convolutional encoder-decoder network. Brain PET images from 129 patients were evaluated. The network was designed to map non-attenuation-corrected (NAC
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2664c583b4b3026aff04a5491397b819
https://doi.org/10.1007/s00330-019-06229-1
https://doi.org/10.1007/s00330-019-06229-1
Publikováno v:
2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).
Coronavirus disease 2019 (COVID-19), a highly contagious respiratory disease, has rapidly become a global pandemic. Chest X-ray imaging could serve an important role in early diagnosis of the disease. Deep learning methods have recently shown promise
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::88c08e0d93077cc2d151094fe1b834ca
https://doi.org/10.1109/nss/mic42677.2020.9508054
https://doi.org/10.1109/nss/mic42677.2020.9508054
Autor:
Kevin H. Leung, Wael Marashdeh, Martin G. Pomper, Rick Wray, Saeed Ashrafinia, Abhinav K. Jha, Arman Rahmim
Publikováno v:
Physics in medicine and biology. 65(24)
An important need exists for reliable positron emission tomography (PET) tumor-segmentation methods for tasks such as PET-based radiation-therapy planning and reliable quantification of volumetric and radiomic features. To address this need, we propo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::349b9c2922728f5ec7d905480233ffea
https://pubmed.ncbi.nlm.nih.gov/32235059
https://pubmed.ncbi.nlm.nih.gov/32235059
Publikováno v:
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures ISBN: 9783030326883
UNSURE/CLIP@MICCAI
UNSURE/CLIP@MICCAI
Deep neural network (DNN) models are widely applied in biomedical image studies since DNN models take advantage of massive data to provide improved performance over traditional machine learning models. However, like any other data-driven models, DNN
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3dff766e308286a669435e8467efd0da
https://doi.org/10.1007/978-3-030-32689-0_7
https://doi.org/10.1007/978-3-030-32689-0_7
Autor:
Arman Rahmim, Kevin H. Leung, Ivan S. Klyuzhin, Abdollah Saberi, M.G. Pomper, Vesna Sossi, Mohammad R. Salmanpour, Abhinav K. Jha, Yong Du
Publikováno v:
2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC).
There are currently no established disease modifying therapies for PD, and prediction of outcome in PD to power clinical studies is a very important area of research. Assessment of PD is informed by imaging the dopamine system with dopamine transport
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::45ac93634cc39ec0b914043080ce10b8
https://doi.org/10.1109/nssmic.2018.8824432
https://doi.org/10.1109/nssmic.2018.8824432
Publikováno v:
Bioconjugate chemistry, vol 26, iss 6
Recent successes in targeted immune and cell-based therapies have driven new directions for pharmaceutical research. With the rise of these new therapies there is an unfilled need for companion diagnostics to assess patients' potential for therapeuti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::42df42969e53240f8e3041c01bba8c04
https://doi.org/10.1021/acs.bioconjchem.5b00144
https://doi.org/10.1021/acs.bioconjchem.5b00144